With a solid grounding in experiments and studies of group behavior (and enlightened common sense), Sunstein explores how groups and societies succeed and fail in what is arguably their most vital task: drawing out and assembling pieces of knowledge that are scattered among many minds. When this process of knowledge integration succeeds, groups can understand, decide, and act with knowledge and wisdom that exceeds that of any of their members.

When knowledge integration fails or goes astray, however, groups can perform worse than even their average members, and sometimes worse than any member.

The results of Suntein’s exploration are sobering, but the opportunities for improvement are staggering. If the knowledge and recommendations that Sunstein offers us were widely known and applied, blunders in group decision making would become substantially less common. These blunders range in size from small to large, and by “large blunders” I mean disastrously wrong decisions at the trillion-dollar, fate-of-nations level.

Infotopia is broad, describing, comparing, and analyzing a range of processes that draw on the power of many minds:

Group deliberation

Polling

Conventional markets

Prediction markets

Open source development

Blogging

Wikis and Wikipedia

Sunstein helps us understand how these process operate and why they work and don’t work under various circumstances. Perhaps the most disturbing result is that deliberative discussion by groups often doesn’t work — that it fails to elicit information from its members, and that, under typical conditions, deliberation is more likely to amplify errors than to correct them.

In addition to diagnosis, Sunstein offers recommendations for improvement, some that a reader can apply next afternoon at work, and others that would involve changing how organizations operate.

Knowledge matters. Decisions matter. If your work or interests involve either, I think you’d enjoy reading Infotopia, and forever after, be glad that you did.

(I recently reviewed another book by Cass Sunstein, Nudge, and with similar enthusiasm.)

Hmmm…I’ll have to check this out. What it brings to mind for me is another recent book called The 4th Paradigm which raises the question of how researchers will (or won’t) deal with the ever larger data sets that have become available (Kryder’s Law gets far less notice than Moore’s observation) via new instruments and new data collection techniques. I have a good sense of this for astronomy but wonder what happens with topics such as climate change or computational chemistry.

…and, of course, all the [biosomething]-omics, where the date sets are not only large, but also extraordinarily interrelated because they describe functional elements of highly integrated systems.

Re. Kryder’s Law, I was reflecting the other day on a recent purchase — a disk drive with a million times the capacity of the first one I’d bought (and less expensive, too). As you note, this exponential progress has been a major enabler of the new large-dataset science. I’ve heard that plans for the data storage and processing system for the Large Synoptic Survey Telescope assume continued exponential progress between now and its 2015 completion date.